Method and system for determining a quality measure for an image using multi-level decomposition of images

1. A method for determining a measure of quality for an image Y, characterizing a similarity between the image Y and a reference image X having same resolution, the method comprising:

(a) applying an N level multiresolution decomposition, comprising levels 1, 2, . . . i, i+1, . . . N, to the image X and the image Y respectively, to produce:

for each level i, with i ranging from 1 to N−1, intermediate subbands of the image X and the image Y for processing at level i+1; and

for the level N, approximation subbands containing respective main contents of the image X and the image Y, and respective detail subbands containing edges of the image X and the image Y;

(b) applying an image quality metric (IQM) to the approximation subband of the image X and the approximation subband of the image Y to produce an approximation quality measure characterizing similarity between the main content of the image X and the main content of the image Y;

(c) at at least one level i, with i ranging from levels 1 to N−1, aggregating the intermediate subbands and the detail subbands, for the image X and the image Y respectively, to produce respective edge maps for the image X and the image Y, characterizing edges of the image X and the image Y respectively;

(d) applying the IQM to the edge maps of the image X and the image Y to produce an edge quality measure characterizing similarity between the edges of the image X and the image Y; and

(e) processing the approximation quality measure and the edge quality measure to determine the measure of quality.

2. The method of claim 1 , wherein the step (c) further comprises:

selecting the intermediate subbands at said at least one level i, with i ranging from 1 to N−1, and the detail subbands, for the image X and the image Y based on an accuracy to be achieved in determining the measure of quality; and

aggregating only selected intermediate subbands and selected detail subbands, for the image X and the image Y.

3. The method of claim 2 , further comprising choosing the selected intermediate subbands at the level i and the selected detail subbands, having substantially same resolution, for aggregating.

4. The method of claim 1 , applying weighting factors to the respective edge maps for the image X and the image Y, for aggregating.

5. The method of claim 2 , further comprising selecting the intermediate subbands at the level i and the detail subbands for the image X and the image Y based on a number of the intermediate and the detailed subbands required for achieving the accuracy.

6. The method of claim 1 , wherein the intermediate subbands at said at least one level i for the image X and the image Y include for each respective image:

level i-detail subbands and level i-wavelet packet (WP) subbands, the level i-WP subbands comprising level i-WP approximation subbands and level i-WP detail subbands.

7. The method of claim 1 , further comprising aggregating one or more of the following:

horizontal intermediate subbands to produce a horizontal edge map;

vertical intermediate subbands to produce a vertical edge map; or

diagonal intermediate subbands to produce a diagonal edge map.

8. The method of claim 1 , wherein the step (a) comprises applying an N level discrete wavelet transform (DWT).

9. The method of claim 1 , wherein the steps (b) and (d) comprise applying the IQM, which is one of the following:

a structural similarity (SSIM) IQM;

an Absolute Difference (AD) IQM;

a peak-signal-to-noise ratio (PSNR) IQM; or

a Visual Information Fidelity (VIF) IQM.

10. The method of claim 1 , further comprising determining N as a function of a minimum size of an approximation subband, which produces a substantially peak response for human visual system.

11. A system for determining a measure of quality for an image Y, characterizing a similarity between the image Y and a reference image X having same resolution, the system comprising:

a processor; and

a non-transitory computer readable storage medium having computer readable instructions stored thereon for execution by the processor, causing the processor to:

(a) apply an N level multiresolution decomposition, comprising levels 1, 2, . . . i, i+1, . . . N, to the image X and the image Y respectively, to produce:

for each level i, with i ranging from 1 to N−1, intermediate subbands of the image X and the image Y for processing at level i+1; and

for the level N, approximation subbands containing respective main contents of the image X and the image Y, and respective detail subbands containing edges of the image X and the image Y;

(b) apply an image quality metric (IQM) to the approximation subband of the image X and the approximation subband of the image Y to produce an approximation quality measure characterizing similarity between the main content of the image X and the main content of the image Y;

(c) at at least one level i, with i ranging from levels 1 to N−1, aggregate the intermediate subbands and the detail subbands, for the image X and the image Y respectively, to produce respective edge maps for the image X and the image Y, characterizing edges of the image X and the image Y respectively;

(d) apply the IQM to the edge maps of the image X and the image Y to produce an edge quality measure characterizing similarity between the edges of the image X and the image Y; and

(e) process the approximation quality measure and the edge quality measure to determine the measure of quality.

12. The system of claim 11 , wherein the computer readable instructions further cause the processor to:

select the intermediate subbands at said at least one level i, with i ranging from 1 to N−1, and the detail subbands, for the image X and the image Y based on an accuracy to be achieved in determining the measure of quality; and

aggregate only selected intermediate subbands and selected detail subbands, for the image X and the image Y.

13. The system of claim 12 , wherein the computer readable instructions further cause the processor to choose the selected intermediate subbands at the level i and the selected detail subbands, having substantially same resolution, for aggregating.

14. The system of claim 12 , wherein the computer readable instructions further cause the processor to apply weighting factors to the respective edge maps for the image X and the image Y, for aggregating.

15. The system of claim 12 , wherein the computer readable instructions further cause the processor to select the intermediate subbands at the level i and the detail subbands for the image X and the image Y based on a number of the intermediate and the detailed subbands required for achieving the accuracy.

16. The system of claim 11 , wherein the intermediate subbands at said at least one level i for the image X and the image Y include for each respective image:

level i-detail subbands and level i-wavelet packet (WP) subbands, the level i-WP subbands comprising level i-WP approximation subbands and level i-WP detail subbands.

17. The method of claim 11 , wherein the computer readable instructions further cause the processor to aggregate one or more of the following:

horizontal intermediate subbands to produce a horizontal edge map;

vertical intermediate subbands to produce a vertical edge map; or

diagonal intermediate subbands to produce a diagonal edge map.

18. The system of claim 11 , wherein the N level multiresolution decomposition is an N level discrete wavelet transform (DWT).

19. The system of claim 11 , wherein the computer readable instructions further cause the processor to apply the IQM, which is one of the following:

a structural similarity (SSIM) IQM;

an Absolute Difference (AD) IQM;

a peak-signal-to-noise ratio (PSNR) IQM; or

a Visual Information Fidelity (VIF) IQM.

20. The system of claim 11 , wherein the computer readable instructions further cause the processor to determine N as a function of a minimum size of an approximation subband, which produces a substantially peak response for human visual system.